from abc import abstractmethod

import math

import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F

from .fp16_util import convert_module_to_f16, convert_module_to_f32
from .nn import (
    checkpoint,
    conv_nd,
    linear,
    avg_pool_nd,
    zero_module,
    normalization,
    timestep_embedding,
)


# from models.submodules import *
import torchvision.models

class VGG19(nn.Module):
    def __init__(self):
        super(VGG19, self).__init__()
        '''
         use vgg19 conv1_2, conv2_2, conv3_3 feature, before relu layer
        '''
        self.feature_list = [7]
        vgg19 = torchvision.models.vgg19(pretrained=True)

        self.model = th.nn.Sequential(*list(vgg19.features.children())[:self.feature_list[-1]+1])
        # self.model.apply(convert_module_to_f16)

    def forward(self, x ,  emb):
        # x = (x-0.5)/0.5
        features = []
        for i, layer in enumerate(list(self.model)):
            # print(layer,i)
            x = layer(x)
            if i in self.feature_list:
                features.append(x)
                # print(x.shape)
        return features



class AttentionPool2d(nn.Module):
    """
    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
    """

    def __init__(
        self,
        spacial_dim: int,
        embed_dim: int,
        num_heads_channels: int,
        output_dim: int = None,
    ):
        super().__init__()
        self.positional_embedding = nn.Parameter(
            th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
        )
        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
        self.num_heads = embed_dim // num_heads_channels
        self.attention = QKVAttention(self.num_heads)

    def forward(self, x):
        b, c, *_spatial = x.shape
        x = x.reshape(b, c, -1)  # NC(HW)
        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
        x = self.qkv_proj(x)
        x = self.attention(x)
        x = self.c_proj(x)
        return x[:, :, 0]


class TimestepBlock(nn.Module):
    """
    Any module where forward() takes timestep embeddings as a second argument.
    """

    @abstractmethod
    def forward(self, x, emb):
        """
        Apply the module to `x` given `emb` timestep embeddings.
        """


class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    """
    A sequential module that passes timestep embeddings to the children that
    support it as an extra input.
    """

    def forward(self, x, emb):
        for layer in self:
            if isinstance(layer, TimestepBlock):
                x = layer(x, emb)
            else:
                x = layer(x)
        return x


class Upsample(nn.Module):
    """
    An upsampling layer with an optional convolution.

    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 upsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        if use_conv:
            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)

    def forward(self, x):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            x = F.interpolate(
                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
            )
        else:
            x = F.interpolate(x, scale_factor=2, mode="nearest")
        if self.use_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.

    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 downsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        stride = 2 if dims != 3 else (1, 2, 2)
        if use_conv:
            self.op = conv_nd(
                dims, self.channels, self.out_channels, 3, stride=stride, padding=1
            )
        else:
            assert self.channels == self.out_channels
            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)


class ResBlock(TimestepBlock):
    """
    A residual block that can optionally change the number of channels.

    :param channels: the number of input channels.
    :param emb_channels: the number of timestep embedding channels.
    :param dropout: the rate of dropout.
    :param out_channels: if specified, the number of out channels.
    :param use_conv: if True and out_channels is specified, use a spatial
        convolution instead of a smaller 1x1 convolution to change the
        channels in the skip connection.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param use_checkpoint: if True, use gradient checkpointing on this module.
    :param up: if True, use this block for upsampling.
    :param down: if True, use this block for downsampling.
    """

    def __init__(
        self,
        channels,
        emb_channels,
        dropout,
        out_channels=None,
        use_conv=False,
        use_scale_shift_norm=False,
        dims=2,
        use_checkpoint=False,
        up=False,
        down=False,
    ):
        super().__init__()
        self.channels = channels
        self.emb_channels = emb_channels
        self.dropout = dropout
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_checkpoint = use_checkpoint
        self.use_scale_shift_norm = use_scale_shift_norm

        self.in_layers = nn.Sequential(
            normalization(channels),
            nn.SiLU(),
            conv_nd(dims, channels, self.out_channels, 3, padding=1),
        )

        self.updown = up or down

        if up:
            self.h_upd = Upsample(channels, False, dims)
            self.x_upd = Upsample(channels, False, dims)
        elif down:
            self.h_upd = Downsample(channels, False, dims)
            self.x_upd = Downsample(channels, False, dims)
        else:
            self.h_upd = self.x_upd = nn.Identity()

        self.emb_layers = nn.Sequential(
            nn.SiLU(),
            linear(
                emb_channels,
                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
            ),
        )
        self.out_layers = nn.Sequential(
            normalization(self.out_channels),
            nn.SiLU(),
            nn.Dropout(p=dropout),
            zero_module(
                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
            ),
        )

        if self.out_channels == channels:
            self.skip_connection = nn.Identity()
        elif use_conv:
            self.skip_connection = conv_nd(
                dims, channels, self.out_channels, 3, padding=1
            )
        else:
            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)

    def forward(self, x, emb):
        """
        Apply the block to a Tensor, conditioned on a timestep embedding.

        :param x: an [N x C x ...] Tensor of features.
        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
        :return: an [N x C x ...] Tensor of outputs.
        """
        return checkpoint(
            self._forward, (x, emb), self.parameters(), self.use_checkpoint
        )

    def _forward(self, x, emb):
        if self.updown:
            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
            h = in_rest(x)
            h = self.h_upd(h)
            x = self.x_upd(x)
            h = in_conv(h)
        else:
            h = self.in_layers(x)
        emb_out = self.emb_layers(emb).type(h.dtype)
        while len(emb_out.shape) < len(h.shape):
            emb_out = emb_out[..., None]
        if self.use_scale_shift_norm:
            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
            scale, shift = th.chunk(emb_out, 2, dim=1)
            h = out_norm(h) * (1 + scale) + shift
            h = out_rest(h)
        else:
            h = h + emb_out
            h = self.out_layers(h)
        return self.skip_connection(x) + h


class AttentionBlock(nn.Module):
    """
    An attention block that allows spatial positions to attend to each other.

    Originally ported from here, but adapted to the N-d case.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
    """

    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        use_checkpoint=False,
        use_new_attention_order=False,
    ):
        super().__init__()
        self.channels = channels
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.use_checkpoint = use_checkpoint
        self.norm = normalization(channels)
        self.qkv = conv_nd(1, channels, channels * 3, 1)
        if use_new_attention_order:
            # split qkv before split heads
            self.attention = QKVAttention(self.num_heads)
        else:
            # split heads before split qkv
            self.attention = QKVAttentionLegacy(self.num_heads)

        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))

    def forward(self, x):
        return checkpoint(self._forward, (x,), self.parameters(), True)

    def _forward(self, x):
        b, c, *spatial = x.shape
        x = x.reshape(b, c, -1)
        qkv = self.qkv(self.norm(x))
        h = self.attention(qkv)
        h = self.proj_out(h)
        return (x + h).reshape(b, c, *spatial)


def count_flops_attn(model, _x, y):
    """
    A counter for the `thop` package to count the operations in an
    attention operation.
    Meant to be used like:
        macs, params = thop.profile(
            model,
            inputs=(inputs, timestamps),
            custom_ops={QKVAttention: QKVAttention.count_flops},
        )
    """
    b, c, *spatial = y[0].shape
    num_spatial = int(np.prod(spatial))
    # We perform two matmuls with the same number of ops.
    # The first computes the weight matrix, the second computes
    # the combination of the value vectors.
    matmul_ops = 2 * b * (num_spatial ** 2) * c
    model.total_ops += th.DoubleTensor([matmul_ops])


class QKVAttentionLegacy(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv):
        """
        Apply QKV attention.

        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts", q * scale, k * scale
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v)
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)


class QKVAttention(nn.Module):
    """
    A module which performs QKV attention and splits in a different order.
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv):
        """
        Apply QKV attention.

        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.chunk(3, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts",
            (q * scale).view(bs * self.n_heads, ch, length),
            (k * scale).view(bs * self.n_heads, ch, length),
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)


class UNetModel(nn.Module):
    """
    The full UNet model with attention and timestep embedding.
    :param in_channels: channels in the input Tensor.
    :param model_channels: base channel count for the model.
    :param out_channels: channels in the output Tensor.
    :param num_res_blocks: number of residual blocks per downsample.
    :param attention_resolutions: a collection of downsample rates at which
        attention will take place. May be a set, list, or tuple.
        For example, if this contains 4, then at 4x downsampling, attention
        will be used.
    :param dropout: the dropout probability.
    :param channel_mult: channel multiplier for each level of the UNet.
    :param conv_resample: if True, use learned convolutions for upsampling and
        downsampling.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param num_classes: if specified (as an int), then this model will be
        class-conditional with `num_classes` classes.
    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
    :param num_heads: the number of attention heads in each attention layer.
    :param num_heads_channels: if specified, ignore num_heads and instead use
                               a fixed channel width per attention head.
    :param num_heads_upsample: works with num_heads to set a different number
                               of heads for upsampling. Deprecated.
    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
    :param resblock_updown: use residual blocks for up/downsampling.
    :param use_new_attention_order: use a different attention pattern for potentially
                                    increased efficiency.
    """

    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        num_classes=None,
        use_checkpoint=False,
        use_fp16=False,
        num_heads=1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
    ):
        super().__init__()

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads
        in_channels=6
        self.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.num_classes = num_classes
        self.use_checkpoint = use_checkpoint
        self.dtype = th.float16 if use_fp16 else th.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample
        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        if self.num_classes is not None:
            self.label_emb = nn.Embedding(num_classes, time_embed_dim)

        ch = input_ch = int(channel_mult[0] * model_channels)
        # print(channel_mult,in_channels)
        # in_channels=6
        # print(in_channels)
        # self.input_transform_1 = conv_nd(2, 6, 3, 3, padding=1)
        self.input_blocks = nn.ModuleList(
            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
        )
        self._feature_size = ch
        input_block_chans = [ch]
        ds = 1
        blah=0
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                # print(level,mult,int(mult * model_channels))

                layers = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=int(mult * model_channels),
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = int(mult * model_channels)
                if ds in attention_resolutions:
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads,
                            num_head_channels=num_head_channels,
                            use_new_attention_order=use_new_attention_order,
                        )
                    )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                blah=blah+1
                # if(blah==1):
                #     ch1=ch+64
                # elif(blah==2):
                #     ch1=ch+128
                # elif(blah==3):
                #     ch1=ch+256
                # else:
                #     ch1=ch
                ch1=ch
                # print(resblock_updown)
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        ResBlock(
                            ch1,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                            )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch
        # print(input_block_chans)
        self.middle_block = TimestepEmbedSequential(
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            AttentionBlock(
                ch,
                use_checkpoint=use_checkpoint,
                num_heads=num_heads,
                num_head_channels=num_head_channels,
                use_new_attention_order=use_new_attention_order,
            ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self._feature_size += ch

        self.output_blocks = nn.ModuleList([])
        for level, mult in list(enumerate(channel_mult))[::-1]:
            for i in range(num_res_blocks + 1):
                ich = input_block_chans.pop()
                layers = [
                    ResBlock(
                        ch + ich,
                        time_embed_dim,
                        dropout,
                        out_channels=int(model_channels * mult),
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = int(model_channels * mult)
                if ds in attention_resolutions:
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads_upsample,
                            num_head_channels=num_head_channels,
                            use_new_attention_order=use_new_attention_order,
                        )
                    )
                if level and i == num_res_blocks:
                    out_ch = ch
                    layers.append(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            up=True,
                        )
                        if resblock_updown
                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
                    )
                    ds //= 2
                self.output_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch

        self.vgg=VGG19()
        # self.conv_convert1 =  ResBlock(
        #                 256,
        #                 time_embed_dim,
        #                 dropout,
        #                 out_channels=192,
        #                 dims=dims,
        #                 use_checkpoint=use_checkpoint,
        #                 use_scale_shift_norm=use_scale_shift_norm,
        #             )
        self.conv_convert2 =  ResBlock(
                        320,
                        time_embed_dim,
                        dropout,
                        out_channels=192,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
        # self.conv_convert3 =  ResBlock(
        #                 640,
        #                 time_embed_dim,
        #                 dropout,
        #                 out_channels=384,
        #                 dims=dims,
        #                 use_checkpoint=use_checkpoint,
        #                 use_scale_shift_norm=use_scale_shift_norm,
        #             )

        self.out = nn.Sequential(
            normalization(ch),
            nn.SiLU(),
            zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
        )
        # print(input_ch,out_channels)
    def convert_to_fp16(self):
        """
        Convert the torso of the model to float16.
        """
        self.vgg.apply(convert_module_to_f16)
        self.input_blocks.apply(convert_module_to_f16)
        self.middle_block.apply(convert_module_to_f16)
        self.output_blocks.apply(convert_module_to_f16)
        # self.conv_convert1.apply(convert_module_to_f16)
        self.conv_convert2.apply(convert_module_to_f16)
        # self.conv_convert3.apply(convert_module_to_f16)
        self.input_transform_1.apply(convert_module_to_f16)


    def convert_to_fp32(self):
        """
        Convert the torso of the model to float32.
        """
        self.vgg.apply(convert_module_to_f32)

        self.input_blocks.apply(convert_module_to_f32)
        self.middle_block.apply(convert_module_to_f32)
        self.output_blocks.apply(convert_module_to_f32)

    def forward(self, x, timesteps, low_res ,high_res,  y=None,**kwargs):
        """
        Apply the model to an input batch.

        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param y: an [N] Tensor of labels, if class-conditional.
        :return: an [N x C x ...] Tensor of outputs.
        """
    
        hs = []
        # x1 = th.cat([x,high_res],1).type(self.dtype)
        # x1 = self.input_transform_1(x)
        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
        # input1=low_res
        high_res=x[:,3:]
        # vgg_feats = self.vgg(high_res.type(self.dtype), emb)
        # vgg_feats1 = self.vgg(high_res.type(self.dtype), emb)

        # print(x.shape)
        # print(emb.shape)
        # vgg_feats=vgg_feats.type(self.dtype)
        # print(vgg_feats[0].shape)
        # print(emb.shape)
        h = x.type(self.dtype)

        for i , module in enumerate(self.input_blocks):
            # print(i,module,h.shape)

            # if(i==3):
            #     # print()
            #     h= th.cat([h,vgg_feats[0]],1)
            #     h = self.conv_convert1(h,emb)
            # if(i==6):        
            #     h= th.cat([h,vgg_feats[0]],1)
            #     h = self.conv_convert2(h,emb)
 
            # if(i==9):
            #     h = th.cat([h,vgg_feats[2]],1)
            #     h = self.conv_convert3(h,emb)
            # print(h.shape)
            # print(h.shape,emb.shape)
            h = module(h, emb)

            hs.append(h)
            # print(h.shape)
        h = self.middle_block(h, emb)
        # stop
        for module in self.output_blocks:
            h = th.cat([h, hs.pop()], dim=1)
            h = module(h, emb)
        h = h.type(x.dtype)
        out=self.out(h)
        return out


class SuperResModel(UNetModel):
    """
    A UNetModel that performs super-resolution.

    Expects an extra kwarg `low_res` to condition on a low-resolution image.
    """

    def __init__(self, image_size, in_channels, *args, **kwargs):
        super().__init__(image_size, in_channels * 2, *args, **kwargs)
        
    def forward(self, x, timesteps, low_res=None, **kwargs):
        _, _, new_height, new_width = x.shape
        low_res = kwargs['SR']
        # upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
        # print(x.shape,low_res.shape)
        # high_res= kwargs['full_res']
        # print(x.shape)
        # low_res=x[:,3:]
        # x = th.cat([x, low_res], dim=1)
        return super().forward(x, timesteps,low_res,low_res, **kwargs)


class EncoderUNetModel(nn.Module):
    """
    The half UNet model with attention and timestep embedding.

    For usage, see UNet.
    """

    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        use_checkpoint=False,
        use_fp16=False,
        num_heads=1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
        pool="adaptive",
    ):
        super().__init__()

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.use_checkpoint = use_checkpoint
        self.dtype = th.float16 if use_fp16 else th.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        ch = int(channel_mult[0] * model_channels)
        self.input_blocks = nn.ModuleList(
            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
        )
        self._feature_size = ch
        input_block_chans = [ch]
        ds = 1
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                layers = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=int(mult * model_channels),
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = int(mult * model_channels)
                if ds in attention_resolutions:
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads,
                            num_head_channels=num_head_channels,
                            use_new_attention_order=use_new_attention_order,
                        )
                    )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        self.middle_block = TimestepEmbedSequential(
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            AttentionBlock(
                ch,
                use_checkpoint=use_checkpoint,
                num_heads=num_heads,
                num_head_channels=num_head_channels,
                use_new_attention_order=use_new_attention_order,
            ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self._feature_size += ch
        self.pool = pool
        if pool == "adaptive":
            self.out = nn.Sequential(
                normalization(ch),
                nn.SiLU(),
                nn.AdaptiveAvgPool2d((1, 1)),
                zero_module(conv_nd(dims, ch, out_channels, 1)),
                nn.Flatten(),
            )
        elif pool == "attention":
            assert num_head_channels != -1
            self.out = nn.Sequential(
                normalization(ch),
                nn.SiLU(),
                AttentionPool2d(
                    (image_size // ds), ch, num_head_channels, out_channels
                ),
            )
        elif pool == "spatial":
            self.out = nn.Sequential(
                nn.Linear(self._feature_size, 2048),
                nn.ReLU(),
                nn.Linear(2048, self.out_channels),
            )
        elif pool == "spatial_v2":
            self.out = nn.Sequential(
                nn.Linear(self._feature_size, 2048),
                normalization(2048),
                nn.SiLU(),
                nn.Linear(2048, self.out_channels),
            )
        else:
            raise NotImplementedError(f"Unexpected {pool} pooling")

    def convert_to_fp16(self):
        """
        Convert the torso of the model to float16.
        """
        self.input_blocks.apply(convert_module_to_f16)
        self.middle_block.apply(convert_module_to_f16)

    def convert_to_fp32(self):
        """
        Convert the torso of the model to float32.
        """
        self.input_blocks.apply(convert_module_to_f32)
        self.middle_block.apply(convert_module_to_f32)

    def forward(self, x, timesteps):
        """
        Apply the model to an input batch.

        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :return: an [N x K] Tensor of outputs.
        """
        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))

        results = []
        h = x.type(self.dtype)
        for module in self.input_blocks:
            h = module(h, emb)
            if self.pool.startswith("spatial"):
                results.append(h.type(x.dtype).mean(dim=(2, 3)))
        h = self.middle_block(h, emb)

        if self.pool.startswith("spatial"):
            results.append(h.type(x.dtype).mean(dim=(2, 3)))
            h = th.cat(results, axis=-1)
            return self.out(h)
        else:
            h = h.type(x.dtype)
            return self.out(h)




# from abc import abstractmethod

# import math

# import numpy as np
# import torch as th
# import torch.nn as nn
# import torch.nn.functional as F

# from .fp16_util import convert_module_to_f16, convert_module_to_f32
# from .nn import (
#     checkpoint,
#     conv_nd,
#     linear,
#     avg_pool_nd,
#     zero_module,
#     normalization,
#     timestep_embedding,
# )


# class AttentionPool2d(nn.Module):
#     """
#     Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
#     """

#     def __init__(
#         self,
#         spacial_dim: int,
#         embed_dim: int,
#         num_heads_channels: int,
#         output_dim: int = None,
#     ):
#         super().__init__()
#         self.positional_embedding = nn.Parameter(
#             th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
#         )
#         self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
#         self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
#         self.num_heads = embed_dim // num_heads_channels
#         self.attention = QKVAttention(self.num_heads)

#     def forward(self, x):
#         b, c, *_spatial = x.shape
#         x = x.reshape(b, c, -1)  # NC(HW)
#         x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
#         x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
#         x = self.qkv_proj(x)
#         x = self.attention(x)
#         x = self.c_proj(x)
#         return x[:, :, 0]


# class TimestepBlock(nn.Module):
#     """
#     Any module where forward() takes timestep embeddings as a second argument.
#     """

#     @abstractmethod
#     def forward(self, x, emb):
#         """
#         Apply the module to `x` given `emb` timestep embeddings.
#         """


# class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
#     """
#     A sequential module that passes timestep embeddings to the children that
#     support it as an extra input.
#     """

#     def forward(self, x, emb):
#         for layer in self:
#             if isinstance(layer, TimestepBlock):
#                 x = layer(x, emb)
#             else:
#                 x = layer(x)
#         return x


# class Upsample(nn.Module):
#     """
#     An upsampling layer with an optional convolution.

#     :param channels: channels in the inputs and outputs.
#     :param use_conv: a bool determining if a convolution is applied.
#     :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
#                  upsampling occurs in the inner-two dimensions.
#     """

#     def __init__(self, channels, use_conv, dims=2, out_channels=None):
#         super().__init__()
#         self.channels = channels
#         self.out_channels = out_channels or channels
#         self.use_conv = use_conv
#         self.dims = dims
#         if use_conv:
#             self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)

#     def forward(self, x):
#         assert x.shape[1] == self.channels
#         if self.dims == 3:
#             x = F.interpolate(
#                 x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
#             )
#         else:
#             x = F.interpolate(x, scale_factor=2, mode="nearest")
#         if self.use_conv:
#             x = self.conv(x)
#         return x


# class Downsample(nn.Module):
#     """
#     A downsampling layer with an optional convolution.

#     :param channels: channels in the inputs and outputs.
#     :param use_conv: a bool determining if a convolution is applied.
#     :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
#                  downsampling occurs in the inner-two dimensions.
#     """

#     def __init__(self, channels, use_conv, dims=2, out_channels=None):
#         super().__init__()
#         self.channels = channels
#         self.out_channels = out_channels or channels
#         self.use_conv = use_conv
#         self.dims = dims
#         stride = 2 if dims != 3 else (1, 2, 2)
#         if use_conv:
#             self.op = conv_nd(
#                 dims, self.channels, self.out_channels, 3, stride=stride, padding=1
#             )
#         else:
#             assert self.channels == self.out_channels
#             self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)

#     def forward(self, x):
#         assert x.shape[1] == self.channels
#         return self.op(x)


# class ResBlock(TimestepBlock):
#     """
#     A residual block that can optionally change the number of channels.

#     :param channels: the number of input channels.
#     :param emb_channels: the number of timestep embedding channels.
#     :param dropout: the rate of dropout.
#     :param out_channels: if specified, the number of out channels.
#     :param use_conv: if True and out_channels is specified, use a spatial
#         convolution instead of a smaller 1x1 convolution to change the
#         channels in the skip connection.
#     :param dims: determines if the signal is 1D, 2D, or 3D.
#     :param use_checkpoint: if True, use gradient checkpointing on this module.
#     :param up: if True, use this block for upsampling.
#     :param down: if True, use this block for downsampling.
#     """

#     def __init__(
#         self,
#         channels,
#         emb_channels,
#         dropout,
#         out_channels=None,
#         use_conv=False,
#         use_scale_shift_norm=False,
#         dims=2,
#         use_checkpoint=False,
#         up=False,
#         down=False,
#     ):
#         super().__init__()
#         self.channels = channels
#         self.emb_channels = emb_channels
#         self.dropout = dropout
#         self.out_channels = out_channels or channels
#         self.use_conv = use_conv
#         self.use_checkpoint = use_checkpoint
#         self.use_scale_shift_norm = use_scale_shift_norm

#         self.in_layers = nn.Sequential(
#             normalization(channels),
#             nn.SiLU(),
#             conv_nd(dims, channels, self.out_channels, 3, padding=1),
#         )

#         self.updown = up or down

#         if up:
#             self.h_upd = Upsample(channels, False, dims)
#             self.x_upd = Upsample(channels, False, dims)
#         elif down:
#             self.h_upd = Downsample(channels, False, dims)
#             self.x_upd = Downsample(channels, False, dims)
#         else:
#             self.h_upd = self.x_upd = nn.Identity()

#         self.emb_layers = nn.Sequential(
#             nn.SiLU(),
#             linear(
#                 emb_channels,
#                 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
#             ),
#         )
#         self.out_layers = nn.Sequential(
#             normalization(self.out_channels),
#             nn.SiLU(),
#             nn.Dropout(p=dropout),
#             zero_module(
#                 conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
#             ),
#         )

#         if self.out_channels == channels:
#             self.skip_connection = nn.Identity()
#         elif use_conv:
#             self.skip_connection = conv_nd(
#                 dims, channels, self.out_channels, 3, padding=1
#             )
#         else:
#             self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)

#     def forward(self, x, emb):
#         """
#         Apply the block to a Tensor, conditioned on a timestep embedding.

#         :param x: an [N x C x ...] Tensor of features.
#         :param emb: an [N x emb_channels] Tensor of timestep embeddings.
#         :return: an [N x C x ...] Tensor of outputs.
#         """
#         return checkpoint(
#             self._forward, (x, emb), self.parameters(), self.use_checkpoint
#         )

#     def _forward(self, x, emb):
#         if self.updown:
#             in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
#             h = in_rest(x)
#             h = self.h_upd(h)
#             x = self.x_upd(x)
#             h = in_conv(h)
#         else:
#             h = self.in_layers(x)
#         emb_out = self.emb_layers(emb).type(h.dtype)
#         while len(emb_out.shape) < len(h.shape):
#             emb_out = emb_out[..., None]
#         if self.use_scale_shift_norm:
#             out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
#             scale, shift = th.chunk(emb_out, 2, dim=1)
#             h = out_norm(h) * (1 + scale) + shift
#             h = out_rest(h)
#         else:
#             h = h + emb_out
#             h = self.out_layers(h)
#         return self.skip_connection(x) + h


# class AttentionBlock(nn.Module):
#     """
#     An attention block that allows spatial positions to attend to each other.

#     Originally ported from here, but adapted to the N-d case.
#     https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
#     """

#     def __init__(
#         self,
#         channels,
#         num_heads=1,
#         num_head_channels=-1,
#         use_checkpoint=False,
#         use_new_attention_order=False,
#     ):
#         super().__init__()
#         self.channels = channels
#         if num_head_channels == -1:
#             self.num_heads = num_heads
#         else:
#             assert (
#                 channels % num_head_channels == 0
#             ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
#             self.num_heads = channels // num_head_channels
#         self.use_checkpoint = use_checkpoint
#         self.norm = normalization(channels)
#         self.qkv = conv_nd(1, channels, channels * 3, 1)
#         if use_new_attention_order:
#             # split qkv before split heads
#             self.attention = QKVAttention(self.num_heads)
#         else:
#             # split heads before split qkv
#             self.attention = QKVAttentionLegacy(self.num_heads)

#         self.proj_out = zero_module(conv_nd(1, channels, channels, 1))

#     def forward(self, x):
#         return checkpoint(self._forward, (x,), self.parameters(), True)

#     def _forward(self, x):
#         b, c, *spatial = x.shape
#         x = x.reshape(b, c, -1)
#         qkv = self.qkv(self.norm(x))
#         h = self.attention(qkv)
#         h = self.proj_out(h)
#         return (x + h).reshape(b, c, *spatial)


# def count_flops_attn(model, _x, y):
#     """
#     A counter for the `thop` package to count the operations in an
#     attention operation.
#     Meant to be used like:
#         macs, params = thop.profile(
#             model,
#             inputs=(inputs, timestamps),
#             custom_ops={QKVAttention: QKVAttention.count_flops},
#         )
#     """
#     b, c, *spatial = y[0].shape
#     num_spatial = int(np.prod(spatial))
#     # We perform two matmuls with the same number of ops.
#     # The first computes the weight matrix, the second computes
#     # the combination of the value vectors.
#     matmul_ops = 2 * b * (num_spatial ** 2) * c
#     model.total_ops += th.DoubleTensor([matmul_ops])


# class QKVAttentionLegacy(nn.Module):
#     """
#     A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
#     """

#     def __init__(self, n_heads):
#         super().__init__()
#         self.n_heads = n_heads

#     def forward(self, qkv):
#         """
#         Apply QKV attention.

#         :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
#         :return: an [N x (H * C) x T] tensor after attention.
#         """
#         bs, width, length = qkv.shape
#         assert width % (3 * self.n_heads) == 0
#         ch = width // (3 * self.n_heads)
#         q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
#         scale = 1 / math.sqrt(math.sqrt(ch))
#         weight = th.einsum(
#             "bct,bcs->bts", q * scale, k * scale
#         )  # More stable with f16 than dividing afterwards
#         weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
#         a = th.einsum("bts,bcs->bct", weight, v)
#         return a.reshape(bs, -1, length)

#     @staticmethod
#     def count_flops(model, _x, y):
#         return count_flops_attn(model, _x, y)


# class QKVAttention(nn.Module):
#     """
#     A module which performs QKV attention and splits in a different order.
#     """

#     def __init__(self, n_heads):
#         super().__init__()
#         self.n_heads = n_heads

#     def forward(self, qkv):
#         """
#         Apply QKV attention.

#         :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
#         :return: an [N x (H * C) x T] tensor after attention.
#         """
#         bs, width, length = qkv.shape
#         assert width % (3 * self.n_heads) == 0
#         ch = width // (3 * self.n_heads)
#         q, k, v = qkv.chunk(3, dim=1)
#         scale = 1 / math.sqrt(math.sqrt(ch))
#         weight = th.einsum(
#             "bct,bcs->bts",
#             (q * scale).view(bs * self.n_heads, ch, length),
#             (k * scale).view(bs * self.n_heads, ch, length),
#         )  # More stable with f16 than dividing afterwards
#         weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
#         a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
#         return a.reshape(bs, -1, length)

#     @staticmethod
#     def count_flops(model, _x, y):
#         return count_flops_attn(model, _x, y)


# class UNetModel(nn.Module):
#     """
#     The full UNet model with attention and timestep embedding.

#     :param in_channels: channels in the input Tensor.
#     :param model_channels: base channel count for the model.
#     :param out_channels: channels in the output Tensor.
#     :param num_res_blocks: number of residual blocks per downsample.
#     :param attention_resolutions: a collection of downsample rates at which
#         attention will take place. May be a set, list, or tuple.
#         For example, if this contains 4, then at 4x downsampling, attention
#         will be used.
#     :param dropout: the dropout probability.
#     :param channel_mult: channel multiplier for each level of the UNet.
#     :param conv_resample: if True, use learned convolutions for upsampling and
#         downsampling.
#     :param dims: determines if the signal is 1D, 2D, or 3D.
#     :param num_classes: if specified (as an int), then this model will be
#         class-conditional with `num_classes` classes.
#     :param use_checkpoint: use gradient checkpointing to reduce memory usage.
#     :param num_heads: the number of attention heads in each attention layer.
#     :param num_heads_channels: if specified, ignore num_heads and instead use
#                                a fixed channel width per attention head.
#     :param num_heads_upsample: works with num_heads to set a different number
#                                of heads for upsampling. Deprecated.
#     :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
#     :param resblock_updown: use residual blocks for up/downsampling.
#     :param use_new_attention_order: use a different attention pattern for potentially
#                                     increased efficiency.
#     """

#     def __init__(
#         self,
#         image_size,
#         in_channels,
#         model_channels,
#         out_channels,
#         num_res_blocks,
#         attention_resolutions,
#         dropout=0,
#         channel_mult=(1, 2, 4, 8),
#         conv_resample=True,
#         dims=2,
#         num_classes=None,
#         use_checkpoint=False,
#         use_fp16=False,
#         num_heads=1,
#         num_head_channels=-1,
#         num_heads_upsample=-1,
#         use_scale_shift_norm=False,
#         resblock_updown=False,
#         use_new_attention_order=False,
#     ):
#         super().__init__()

#         if num_heads_upsample == -1:
#             num_heads_upsample = num_heads

#         self.image_size = image_size
#         self.in_channels = in_channels
#         self.model_channels = model_channels
#         self.out_channels = out_channels
#         self.num_res_blocks = num_res_blocks
#         self.attention_resolutions = attention_resolutions
#         self.dropout = dropout
#         self.channel_mult = channel_mult
#         self.conv_resample = conv_resample
#         self.num_classes = num_classes
#         self.use_checkpoint = use_checkpoint
#         self.dtype = th.float16 if use_fp16 else th.float32
#         self.num_heads = num_heads
#         self.num_head_channels = num_head_channels
#         self.num_heads_upsample = num_heads_upsample

#         time_embed_dim = model_channels * 4
#         self.time_embed = nn.Sequential(
#             linear(model_channels, time_embed_dim),
#             nn.SiLU(),
#             linear(time_embed_dim, time_embed_dim),
#         )

#         if self.num_classes is not None:
#             self.label_emb = nn.Embedding(num_classes, time_embed_dim)

#         ch = input_ch = int(channel_mult[0] * model_channels)
#         self.input_blocks = nn.ModuleList(
#             [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
#         )
#         self._feature_size = ch
#         input_block_chans = [ch]
#         ds = 1
#         for level, mult in enumerate(channel_mult):
#             for _ in range(num_res_blocks):
#                 layers = [
#                     ResBlock(
#                         ch,
#                         time_embed_dim,
#                         dropout,
#                         out_channels=int(mult * model_channels),
#                         dims=dims,
#                         use_checkpoint=use_checkpoint,
#                         use_scale_shift_norm=use_scale_shift_norm,
#                     )
#                 ]
#                 ch = int(mult * model_channels)
#                 if ds in attention_resolutions:
#                     layers.append(
#                         AttentionBlock(
#                             ch,
#                             use_checkpoint=use_checkpoint,
#                             num_heads=num_heads,
#                             num_head_channels=num_head_channels,
#                             use_new_attention_order=use_new_attention_order,
#                         )
#                     )
#                 self.input_blocks.append(TimestepEmbedSequential(*layers))
#                 self._feature_size += ch
#                 input_block_chans.append(ch)
#             if level != len(channel_mult) - 1:
#                 out_ch = ch
#                 self.input_blocks.append(
#                     TimestepEmbedSequential(
#                         ResBlock(
#                             ch,
#                             time_embed_dim,
#                             dropout,
#                             out_channels=out_ch,
#                             dims=dims,
#                             use_checkpoint=use_checkpoint,
#                             use_scale_shift_norm=use_scale_shift_norm,
#                             down=True,
#                         )
#                         if resblock_updown
#                         else Downsample(
#                             ch, conv_resample, dims=dims, out_channels=out_ch
#                         )
#                     )
#                 )
#                 ch = out_ch
#                 input_block_chans.append(ch)
#                 ds *= 2
#                 self._feature_size += ch

#         self.middle_block = TimestepEmbedSequential(
#             ResBlock(
#                 ch,
#                 time_embed_dim,
#                 dropout,
#                 dims=dims,
#                 use_checkpoint=use_checkpoint,
#                 use_scale_shift_norm=use_scale_shift_norm,
#             ),
#             AttentionBlock(
#                 ch,
#                 use_checkpoint=use_checkpoint,
#                 num_heads=num_heads,
#                 num_head_channels=num_head_channels,
#                 use_new_attention_order=use_new_attention_order,
#             ),
#             ResBlock(
#                 ch,
#                 time_embed_dim,
#                 dropout,
#                 dims=dims,
#                 use_checkpoint=use_checkpoint,
#                 use_scale_shift_norm=use_scale_shift_norm,
#             ),
#         )
#         self._feature_size += ch

#         self.output_blocks = nn.ModuleList([])
#         for level, mult in list(enumerate(channel_mult))[::-1]:
#             for i in range(num_res_blocks + 1):
#                 ich = input_block_chans.pop()
#                 layers = [
#                     ResBlock(
#                         ch + ich,
#                         time_embed_dim,
#                         dropout,
#                         out_channels=int(model_channels * mult),
#                         dims=dims,
#                         use_checkpoint=use_checkpoint,
#                         use_scale_shift_norm=use_scale_shift_norm,
#                     )
#                 ]
#                 ch = int(model_channels * mult)
#                 if ds in attention_resolutions:
#                     layers.append(
#                         AttentionBlock(
#                             ch,
#                             use_checkpoint=use_checkpoint,
#                             num_heads=num_heads_upsample,
#                             num_head_channels=num_head_channels,
#                             use_new_attention_order=use_new_attention_order,
#                         )
#                     )
#                 if level and i == num_res_blocks:
#                     out_ch = ch
#                     layers.append(
#                         ResBlock(
#                             ch,
#                             time_embed_dim,
#                             dropout,
#                             out_channels=out_ch,
#                             dims=dims,
#                             use_checkpoint=use_checkpoint,
#                             use_scale_shift_norm=use_scale_shift_norm,
#                             up=True,
#                         )
#                         if resblock_updown
#                         else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
#                     )
#                     ds //= 2
#                 self.output_blocks.append(TimestepEmbedSequential(*layers))
#                 self._feature_size += ch

#         self.out = nn.Sequential(
#             normalization(ch),
#             nn.SiLU(),
#             zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
#         )

#     def convert_to_fp16(self):
#         """
#         Convert the torso of the model to float16.
#         """
#         self.input_blocks.apply(convert_module_to_f16)
#         self.middle_block.apply(convert_module_to_f16)
#         self.output_blocks.apply(convert_module_to_f16)

#     def convert_to_fp32(self):
#         """
#         Convert the torso of the model to float32.
#         """
#         self.input_blocks.apply(convert_module_to_f32)
#         self.middle_block.apply(convert_module_to_f32)
#         self.output_blocks.apply(convert_module_to_f32)

#     def forward(self, x, timesteps,y=None,  **kwargs):
#         """
#         Apply the model to an input batch.

#         :param x: an [N x C x ...] Tensor of inputs.
#         :param timesteps: a 1-D batch of timesteps.
#         :param y: an [N] Tensor of labels, if class-conditional.
#         :return: an [N x C x ...] Tensor of outputs.
#         """
#         # y=None
#         # assert (y is not None) == (
#         #     self.num_classes is not None
#         # ), "must specify y if and only if the model is class-conditional"

#         hs = []
#         emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))

#         # if self.num_classes is not None:
#         #     assert y.shape == (x.shape[0],)
#         #     emb = emb + self.label_emb(y)

#         h = x.type(self.dtype)
#         for module in self.input_blocks:
#             h = module(h, emb)
#             hs.append(h)
#         h = self.middle_block(h, emb)
#         for module in self.output_blocks:
#             h = th.cat([h, hs.pop()], dim=1)
#             h = module(h, emb)
#         h = h.type(x.dtype)
#         return self.out(h)


# class SuperResModel(UNetModel):
#     """
#     A UNetModel that performs super-resolution.

#     Expects an extra kwarg `low_res` to condition on a low-resolution image.
#     """

#     def __init__(self, image_size, in_channels, *args, **kwargs):
#         super().__init__(image_size, in_channels * 2, *args, **kwargs)

#     def forward(self, x, timesteps, low_res=None, **kwargs):
#         _, _, new_height, new_width = x.shape
#         # upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
#         # for _ in kwargs:
#         #     print(_)
#         # stop
#         # upsampled = kwargs["SR"]
#         # x = th.cat([x, upsampled], dim=1)
#         return super().forward(x, timesteps, **kwargs)


# class EncoderUNetModel(nn.Module):
#     """
#     The half UNet model with attention and timestep embedding.

#     For usage, see UNet.
#     """

#     def __init__(
#         self,
#         image_size,
#         in_channels,
#         model_channels,
#         out_channels,
#         num_res_blocks,
#         attention_resolutions,
#         dropout=0,
#         channel_mult=(1, 2, 4, 8),
#         conv_resample=True,
#         dims=2,
#         use_checkpoint=False,
#         use_fp16=False,
#         num_heads=1,
#         num_head_channels=-1,
#         num_heads_upsample=-1,
#         use_scale_shift_norm=False,
#         resblock_updown=False,
#         use_new_attention_order=False,
#         pool="adaptive",
#     ):
#         super().__init__()

#         if num_heads_upsample == -1:
#             num_heads_upsample = num_heads

#         self.in_channels = in_channels
#         self.model_channels = model_channels
#         self.out_channels = out_channels
#         self.num_res_blocks = num_res_blocks
#         self.attention_resolutions = attention_resolutions
#         self.dropout = dropout
#         self.channel_mult = channel_mult
#         self.conv_resample = conv_resample
#         self.use_checkpoint = use_checkpoint
#         self.dtype = th.float16 if use_fp16 else th.float32
#         self.num_heads = num_heads
#         self.num_head_channels = num_head_channels
#         self.num_heads_upsample = num_heads_upsample

#         time_embed_dim = model_channels * 4
#         self.time_embed = nn.Sequential(
#             linear(model_channels, time_embed_dim),
#             nn.SiLU(),
#             linear(time_embed_dim, time_embed_dim),
#         )

#         ch = int(channel_mult[0] * model_channels)
#         self.input_blocks = nn.ModuleList(
#             [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
#         )
#         self._feature_size = ch
#         input_block_chans = [ch]
#         ds = 1
#         for level, mult in enumerate(channel_mult):
#             for _ in range(num_res_blocks):
#                 layers = [
#                     ResBlock(
#                         ch,
#                         time_embed_dim,
#                         dropout,
#                         out_channels=int(mult * model_channels),
#                         dims=dims,
#                         use_checkpoint=use_checkpoint,
#                         use_scale_shift_norm=use_scale_shift_norm,
#                     )
#                 ]
#                 ch = int(mult * model_channels)
#                 if ds in attention_resolutions:
#                     layers.append(
#                         AttentionBlock(
#                             ch,
#                             use_checkpoint=use_checkpoint,
#                             num_heads=num_heads,
#                             num_head_channels=num_head_channels,
#                             use_new_attention_order=use_new_attention_order,
#                         )
#                     )
#                 self.input_blocks.append(TimestepEmbedSequential(*layers))
#                 self._feature_size += ch
#                 input_block_chans.append(ch)
#             if level != len(channel_mult) - 1:
#                 out_ch = ch
#                 self.input_blocks.append(
#                     TimestepEmbedSequential(
#                         ResBlock(
#                             ch,
#                             time_embed_dim,
#                             dropout,
#                             out_channels=out_ch,
#                             dims=dims,
#                             use_checkpoint=use_checkpoint,
#                             use_scale_shift_norm=use_scale_shift_norm,
#                             down=True,
#                         )
#                         if resblock_updown
#                         else Downsample(
#                             ch, conv_resample, dims=dims, out_channels=out_ch
#                         )
#                     )
#                 )
#                 ch = out_ch
#                 input_block_chans.append(ch)
#                 ds *= 2
#                 self._feature_size += ch

#         self.middle_block = TimestepEmbedSequential(
#             ResBlock(
#                 ch,
#                 time_embed_dim,
#                 dropout,
#                 dims=dims,
#                 use_checkpoint=use_checkpoint,
#                 use_scale_shift_norm=use_scale_shift_norm,
#             ),
#             AttentionBlock(
#                 ch,
#                 use_checkpoint=use_checkpoint,
#                 num_heads=num_heads,
#                 num_head_channels=num_head_channels,
#                 use_new_attention_order=use_new_attention_order,
#             ),
#             ResBlock(
#                 ch,
#                 time_embed_dim,
#                 dropout,
#                 dims=dims,
#                 use_checkpoint=use_checkpoint,
#                 use_scale_shift_norm=use_scale_shift_norm,
#             ),
#         )
#         self._feature_size += ch
#         self.pool = pool
#         if pool == "adaptive":
#             self.out = nn.Sequential(
#                 normalization(ch),
#                 nn.SiLU(),
#                 nn.AdaptiveAvgPool2d((1, 1)),
#                 zero_module(conv_nd(dims, ch, out_channels, 1)),
#                 nn.Flatten(),
#             )
#         elif pool == "attention":
#             assert num_head_channels != -1
#             self.out = nn.Sequential(
#                 normalization(ch),
#                 nn.SiLU(),
#                 AttentionPool2d(
#                     (image_size // ds), ch, num_head_channels, out_channels
#                 ),
#             )
#         elif pool == "spatial":
#             self.out = nn.Sequential(
#                 nn.Linear(self._feature_size, 2048),
#                 nn.ReLU(),
#                 nn.Linear(2048, self.out_channels),
#             )
#         elif pool == "spatial_v2":
#             self.out = nn.Sequential(
#                 nn.Linear(self._feature_size, 2048),
#                 normalization(2048),
#                 nn.SiLU(),
#                 nn.Linear(2048, self.out_channels),
#             )
#         else:
#             raise NotImplementedError(f"Unexpected {pool} pooling")

#     def convert_to_fp16(self):
#         """
#         Convert the torso of the model to float16.
#         """
#         self.input_blocks.apply(convert_module_to_f16)
#         self.middle_block.apply(convert_module_to_f16)

#     def convert_to_fp32(self):
#         """
#         Convert the torso of the model to float32.
#         """
#         self.input_blocks.apply(convert_module_to_f32)
#         self.middle_block.apply(convert_module_to_f32)

#     def forward(self, x, timesteps):
#         """
#         Apply the model to an input batch.

#         :param x: an [N x C x ...] Tensor of inputs.
#         :param timesteps: a 1-D batch of timesteps.goo
#         :return: an [N x K] Tensor of outputs.
#         """
#         emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))

#         results = []
#         h = x.type(self.dtype)
#         for module in self.input_blocks:
#             h = module(h, emb)
#             if self.pool.startswith("spatial"):
#                 results.append(h.type(x.dtype).mean(dim=(2, 3)))
#         h = self.middle_block(h, emb)
#         if self.pool.startswith("spatial"):
#             results.append(h.type(x.dtype).mean(dim=(2, 3)))
#             h = th.cat(results, axis=-1)
#             return self.out(h)
#         else:
#             h = h.type(x.dtype)
#             return self.out(h)
